We propose CLIP-Actor, a text-driven motion recommendation and neural mesh
stylization system for human mesh animation. CLIP-Actor animates a 3D human
mesh to conform to a text prompt by recommending a motion sequence and
optimizing mesh style attributes. We build a text-driven human motion
recommendation system by leveraging a large-scale human motion dataset with
language labels. Given a natural language prompt, CLIP-Actor suggests a
text-conforming human motion in a coarse-to-fine manner. Then, our novel
zero-shot neural style optimization detailizes and texturizes the recommended
mesh sequence to conform to the prompt in a temporally-consistent and
pose-agnostic manner. This is distinctive in that prior work fails to generate
plausible results when the pose of an artist-designed mesh does not conform to
the text from the beginning. We further propose the spatio-temporal view
augmentation and mask-weighted embedding attention, which stabilize the
optimization process by leveraging multi-frame human motion and rejecting
poorly rendered views. We demonstrate that CLIP-Actor produces plausible and
human-recognizable style 3D human mesh in motion with detailed geometry and
texture solely from a natural language prompt.

提出了 CLIP-Actor：一种基于文本的动作推荐和神经网格风格化系统，能够根据文本提示为 3D 人类网格动画建议运动序列和优化网格样式属性，这对于以前无法生成符合自然语言提示的 3D 人物运动效果是非常独特的，CLIP-Actor 可以仅仅通过自然语言提示产生具有细节几何和纹理的真人可识别的风格 3D 人类网格运动。